Overview

Dataset statistics

Number of variables13
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.2 KiB
Average record size in memory104.7 B

Variable types

Numeric13

Alerts

alcohol is highly correlated with color_intensity and 1 other fieldsHigh correlation
malic_acid is highly correlated with hueHigh correlation
magnesium is highly correlated with prolineHigh correlation
total_phenols is highly correlated with flavanoids and 2 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 4 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with flavanoidsHigh correlation
proanthocyanins is highly correlated with total_phenols and 2 other fieldsHigh correlation
color_intensity is highly correlated with alcoholHigh correlation
hue is highly correlated with malic_acid and 1 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with total_phenols and 2 other fieldsHigh correlation
proline is highly correlated with alcohol and 1 other fieldsHigh correlation
alcohol is highly correlated with color_intensity and 1 other fieldsHigh correlation
malic_acid is highly correlated with hueHigh correlation
total_phenols is highly correlated with flavanoids and 2 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 4 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with flavanoids and 1 other fieldsHigh correlation
proanthocyanins is highly correlated with total_phenols and 2 other fieldsHigh correlation
color_intensity is highly correlated with alcohol and 1 other fieldsHigh correlation
hue is highly correlated with malic_acid and 3 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with total_phenols and 4 other fieldsHigh correlation
proline is highly correlated with alcoholHigh correlation
total_phenols is highly correlated with flavanoidsHigh correlation
flavanoids is highly correlated with total_phenols and 2 other fieldsHigh correlation
proanthocyanins is highly correlated with flavanoidsHigh correlation
od280/od315_of_diluted_wines is highly correlated with flavanoidsHigh correlation
alcohol is highly correlated with proanthocyanins and 3 other fieldsHigh correlation
malic_acid is highly correlated with total_phenolsHigh correlation
ash is highly correlated with alcalinity_of_ashHigh correlation
alcalinity_of_ash is highly correlated with ash and 2 other fieldsHigh correlation
magnesium is highly correlated with proanthocyanins and 1 other fieldsHigh correlation
total_phenols is highly correlated with malic_acid and 4 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 5 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with od280/od315_of_diluted_winesHigh correlation
proanthocyanins is highly correlated with alcohol and 5 other fieldsHigh correlation
color_intensity is highly correlated with alcohol and 3 other fieldsHigh correlation
hue is highly correlated with alcohol and 2 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with total_phenols and 4 other fieldsHigh correlation
proline is highly correlated with alcohol and 6 other fieldsHigh correlation

Reproduction

Analysis started2022-07-18 10:05:33.569666
Analysis finished2022-07-18 10:06:07.464083
Duration33.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

alcohol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.00061798
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:07.622702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.811826538
Coefficient of variation (CV)0.06244522679
Kurtosis-0.8524995685
Mean13.00061798
Median Absolute Deviation (MAD)0.68
Skewness-0.05148233108
Sum2314.11
Variance0.6590623278
MonotonicityNot monotonic
2022-07-18T11:06:07.898717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.056
 
3.4%
12.376
 
3.4%
12.085
 
2.8%
12.294
 
2.2%
12.423
 
1.7%
12.253
 
1.7%
123
 
1.7%
12.332
 
1.1%
13.172
 
1.1%
13.732
 
1.1%
Other values (116)142
79.8%
ValueCountFrequency (%)
11.031
0.6%
11.411
0.6%
11.451
0.6%
11.461
0.6%
11.561
0.6%
11.611
0.6%
11.621
0.6%
11.641
0.6%
11.651
0.6%
11.661
0.6%
ValueCountFrequency (%)
14.831
0.6%
14.751
0.6%
14.391
0.6%
14.382
1.1%
14.371
0.6%
14.341
0.6%
14.31
0.6%
14.231
0.6%
14.222
1.1%
14.211
0.6%

malic_acid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.336348315
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:08.136703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.117146098
Coefficient of variation (CV)0.478159053
Kurtosis0.2992066799
Mean2.336348315
Median Absolute Deviation (MAD)0.52
Skewness1.039651193
Sum415.87
Variance1.248015403
MonotonicityNot monotonic
2022-07-18T11:06:08.373567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.737
 
3.9%
1.674
 
2.2%
1.814
 
2.2%
1.683
 
1.7%
1.613
 
1.7%
1.513
 
1.7%
1.93
 
1.7%
1.353
 
1.7%
1.533
 
1.7%
1.652
 
1.1%
Other values (123)143
80.3%
ValueCountFrequency (%)
0.741
0.6%
0.891
0.6%
0.91
0.6%
0.921
0.6%
0.942
1.1%
0.981
0.6%
0.991
0.6%
1.011
0.6%
1.071
0.6%
1.091
0.6%
ValueCountFrequency (%)
5.81
0.6%
5.651
0.6%
5.511
0.6%
5.191
0.6%
5.041
0.6%
4.951
0.6%
4.721
0.6%
4.611
0.6%
4.61
0.6%
4.431
0.6%

ash
Real number (ℝ≥0)

HIGH CORRELATION

Distinct79
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.366516854
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:08.675745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.2743440091
Coefficient of variation (CV)0.1159273422
Kurtosis1.143978169
Mean2.366516854
Median Absolute Deviation (MAD)0.16
Skewness-0.1766993165
Sum421.24
Variance0.07526463531
MonotonicityNot monotonic
2022-07-18T11:06:08.908735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.37
 
3.9%
2.287
 
3.9%
2.76
 
3.4%
2.326
 
3.4%
2.366
 
3.4%
2.25
 
2.8%
2.385
 
2.8%
2.485
 
2.8%
2.14
 
2.2%
2.54
 
2.2%
Other values (69)123
69.1%
ValueCountFrequency (%)
1.361
 
0.6%
1.72
1.1%
1.711
 
0.6%
1.751
 
0.6%
1.821
 
0.6%
1.881
 
0.6%
1.91
 
0.6%
1.923
1.7%
1.941
 
0.6%
1.951
 
0.6%
ValueCountFrequency (%)
3.231
0.6%
3.221
0.6%
2.921
0.6%
2.871
0.6%
2.861
0.6%
2.841
0.6%
2.81
0.6%
2.781
0.6%
2.751
0.6%
2.742
1.1%

alcalinity_of_ash
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.49494382
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:09.162082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.339563767
Coefficient of variation (CV)0.171304098
Kurtosis0.4879415405
Mean19.49494382
Median Absolute Deviation (MAD)2.05
Skewness0.2130468864
Sum3470.1
Variance11.15268616
MonotonicityNot monotonic
2022-07-18T11:06:09.387152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015
 
8.4%
1611
 
6.2%
2111
 
6.2%
1810
 
5.6%
199
 
5.1%
21.58
 
4.5%
18.57
 
3.9%
19.57
 
3.9%
227
 
3.9%
22.57
 
3.9%
Other values (53)86
48.3%
ValueCountFrequency (%)
10.61
0.6%
11.21
0.6%
11.41
0.6%
121
0.6%
12.41
0.6%
13.21
0.6%
142
1.1%
14.61
0.6%
14.81
0.6%
152
1.1%
ValueCountFrequency (%)
301
 
0.6%
28.52
 
1.1%
271
 
0.6%
26.51
 
0.6%
261
 
0.6%
25.51
 
0.6%
255
2.8%
24.53
1.7%
245
2.8%
23.61
 
0.6%

magnesium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.74157303
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:09.618277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.28248352
Coefficient of variation (CV)0.1431948894
Kurtosis2.104991324
Mean99.74157303
Median Absolute Deviation (MAD)10
Skewness1.098191055
Sum17754
Variance203.9893354
MonotonicityNot monotonic
2022-07-18T11:06:09.855909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8813
 
7.3%
8611
 
6.2%
989
 
5.1%
1019
 
5.1%
968
 
4.5%
1027
 
3.9%
946
 
3.4%
856
 
3.4%
1126
 
3.4%
975
 
2.8%
Other values (43)98
55.1%
ValueCountFrequency (%)
701
 
0.6%
783
 
1.7%
805
 
2.8%
811
 
0.6%
821
 
0.6%
843
 
1.7%
856
3.4%
8611
6.2%
873
 
1.7%
8813
7.3%
ValueCountFrequency (%)
1621
0.6%
1511
0.6%
1391
0.6%
1361
0.6%
1341
0.6%
1321
0.6%
1281
0.6%
1271
0.6%
1261
0.6%
1241
0.6%

total_phenols
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.29511236
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:10.076752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.6258510488
Coefficient of variation (CV)0.2726886317
Kurtosis-0.8356265234
Mean2.29511236
Median Absolute Deviation (MAD)0.505
Skewness0.0866385864
Sum408.53
Variance0.3916895353
MonotonicityNot monotonic
2022-07-18T11:06:10.310430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28
 
4.5%
2.86
 
3.4%
36
 
3.4%
2.66
 
3.4%
25
 
2.8%
2.955
 
2.8%
1.654
 
2.2%
2.454
 
2.2%
2.854
 
2.2%
1.384
 
2.2%
Other values (87)126
70.8%
ValueCountFrequency (%)
0.981
 
0.6%
1.11
 
0.6%
1.151
 
0.6%
1.251
 
0.6%
1.281
 
0.6%
1.31
 
0.6%
1.351
 
0.6%
1.384
2.2%
1.392
1.1%
1.42
1.1%
ValueCountFrequency (%)
3.881
 
0.6%
3.851
 
0.6%
3.521
 
0.6%
3.51
 
0.6%
3.41
 
0.6%
3.381
 
0.6%
3.33
1.7%
3.271
 
0.6%
3.252
1.1%
3.21
 
0.6%

flavanoids
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029269663
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:10.558392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.998858685
Coefficient of variation (CV)0.4922257023
Kurtosis-0.8803815472
Mean2.029269663
Median Absolute Deviation (MAD)0.835
Skewness0.02534355338
Sum361.21
Variance0.9977186726
MonotonicityNot monotonic
2022-07-18T11:06:10.795494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.654
 
2.2%
2.033
 
1.7%
2.683
 
1.7%
0.63
 
1.7%
1.253
 
1.7%
0.583
 
1.7%
2.532
 
1.1%
0.472
 
1.1%
0.662
 
1.1%
2.922
 
1.1%
Other values (122)151
84.8%
ValueCountFrequency (%)
0.341
0.6%
0.472
1.1%
0.481
0.6%
0.491
0.6%
0.52
1.1%
0.511
0.6%
0.521
0.6%
0.551
0.6%
0.561
0.6%
0.571
0.6%
ValueCountFrequency (%)
5.081
0.6%
3.931
0.6%
3.751
0.6%
3.741
0.6%
3.691
0.6%
3.671
0.6%
3.641
0.6%
3.561
0.6%
3.541
0.6%
3.491
0.6%

nonflavanoid_phenols
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3618539326
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:11.012766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.1244533403
Coefficient of variation (CV)0.3439325349
Kurtosis-0.6371910641
Mean0.3618539326
Median Absolute Deviation (MAD)0.085
Skewness0.4501513356
Sum64.41
Variance0.01548863391
MonotonicityNot monotonic
2022-07-18T11:06:11.230659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.4311
 
6.2%
0.2611
 
6.2%
0.2910
 
5.6%
0.329
 
5.1%
0.278
 
4.5%
0.38
 
4.5%
0.348
 
4.5%
0.48
 
4.5%
0.378
 
4.5%
0.247
 
3.9%
Other values (29)90
50.6%
ValueCountFrequency (%)
0.131
 
0.6%
0.142
 
1.1%
0.175
2.8%
0.192
 
1.1%
0.22
 
1.1%
0.216
3.4%
0.226
3.4%
0.247
3.9%
0.252
 
1.1%
0.2611
6.2%
ValueCountFrequency (%)
0.661
 
0.6%
0.634
2.2%
0.613
1.7%
0.63
1.7%
0.583
1.7%
0.561
 
0.6%
0.551
 
0.6%
0.537
3.9%
0.525
2.8%
0.55
2.8%

proanthocyanins
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.590898876
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:11.446415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5723588627
Coefficient of variation (CV)0.3597707379
Kurtosis0.5546485226
Mean1.590898876
Median Absolute Deviation (MAD)0.38
Skewness0.5171371723
Sum283.18
Variance0.3275946677
MonotonicityNot monotonic
2022-07-18T11:06:11.920469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.359
 
5.1%
1.467
 
3.9%
1.876
 
3.4%
1.255
 
2.8%
1.664
 
2.2%
2.084
 
2.2%
1.564
 
2.2%
1.984
 
2.2%
2.293
 
1.7%
1.143
 
1.7%
Other values (91)129
72.5%
ValueCountFrequency (%)
0.411
0.6%
0.422
1.1%
0.551
0.6%
0.621
0.6%
0.642
1.1%
0.681
0.6%
0.732
1.1%
0.751
0.6%
0.82
1.1%
0.811
0.6%
ValueCountFrequency (%)
3.581
 
0.6%
3.281
 
0.6%
2.961
 
0.6%
2.912
1.1%
2.813
1.7%
2.761
 
0.6%
2.71
 
0.6%
2.51
 
0.6%
2.491
 
0.6%
2.451
 
0.6%

color_intensity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.058089882
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:12.130645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.318285872
Coefficient of variation (CV)0.4583322807
Kurtosis0.3815222728
Mean5.058089882
Median Absolute Deviation (MAD)1.51
Skewness0.868584791
Sum900.339999
Variance5.374449383
MonotonicityNot monotonic
2022-07-18T11:06:12.357787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.64
 
2.2%
4.64
 
2.2%
3.84
 
2.2%
3.43
 
1.7%
53
 
1.7%
4.53
 
1.7%
5.43
 
1.7%
5.63
 
1.7%
3.053
 
1.7%
5.73
 
1.7%
Other values (122)145
81.5%
ValueCountFrequency (%)
1.281
0.6%
1.741
0.6%
1.91
0.6%
1.952
1.1%
21
0.6%
2.062
1.1%
2.081
0.6%
2.121
0.6%
2.151
0.6%
2.21
0.6%
ValueCountFrequency (%)
131
0.6%
11.751
0.6%
10.81
0.6%
10.681
0.6%
10.521
0.6%
10.261
0.6%
10.21
0.6%
9.8999991
0.6%
9.71
0.6%
9.581
0.6%

hue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9574494382
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:12.640639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.2285715658
Coefficient of variation (CV)0.2387296464
Kurtosis-0.3440957414
Mean0.9574494382
Median Absolute Deviation (MAD)0.165
Skewness0.0210912722
Sum170.426
Variance0.05224496071
MonotonicityNot monotonic
2022-07-18T11:06:12.891036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.048
 
4.5%
1.237
 
3.9%
1.126
 
3.4%
0.575
 
2.8%
0.895
 
2.8%
0.965
 
2.8%
1.255
 
2.8%
0.754
 
2.2%
1.054
 
2.2%
1.194
 
2.2%
Other values (68)125
70.2%
ValueCountFrequency (%)
0.481
 
0.6%
0.541
 
0.6%
0.551
 
0.6%
0.562
 
1.1%
0.575
2.8%
0.582
 
1.1%
0.592
 
1.1%
0.63
1.7%
0.612
 
1.1%
0.621
 
0.6%
ValueCountFrequency (%)
1.711
 
0.6%
1.451
 
0.6%
1.421
 
0.6%
1.381
 
0.6%
1.362
 
1.1%
1.331
 
0.6%
1.312
 
1.1%
1.282
 
1.1%
1.271
 
0.6%
1.255
2.8%

od280/od315_of_diluted_wines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.611685393
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:13.115613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.7099904288
Coefficient of variation (CV)0.2718514376
Kurtosis-1.086434527
Mean2.611685393
Median Absolute Deviation (MAD)0.52
Skewness-0.307285499
Sum464.88
Variance0.5040864089
MonotonicityNot monotonic
2022-07-18T11:06:13.309827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.875
 
2.8%
1.824
 
2.2%
34
 
2.2%
2.784
 
2.2%
1.563
 
1.7%
1.753
 
1.7%
2.773
 
1.7%
2.313
 
1.7%
1.333
 
1.7%
3.333
 
1.7%
Other values (112)143
80.3%
ValueCountFrequency (%)
1.271
 
0.6%
1.292
1.1%
1.31
 
0.6%
1.333
1.7%
1.361
 
0.6%
1.421
 
0.6%
1.471
 
0.6%
1.481
 
0.6%
1.512
1.1%
1.551
 
0.6%
ValueCountFrequency (%)
41
0.6%
3.921
0.6%
3.821
0.6%
3.711
0.6%
3.691
0.6%
3.641
0.6%
3.631
0.6%
3.591
0.6%
3.582
1.1%
3.571
0.6%

proline
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8932584
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-07-18T11:06:13.513571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.9074743
Coefficient of variation (CV)0.4216231312
Kurtosis-0.2484031061
Mean746.8932584
Median Absolute Deviation (MAD)202.5
Skewness0.7678217814
Sum132947
Variance99166.71736
MonotonicityNot monotonic
2022-07-18T11:06:13.713776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6805
 
2.8%
5205
 
2.8%
6254
 
2.2%
7504
 
2.2%
6304
 
2.2%
10353
 
1.7%
5623
 
1.7%
4953
 
1.7%
6603
 
1.7%
5103
 
1.7%
Other values (111)141
79.2%
ValueCountFrequency (%)
2781
0.6%
2901
0.6%
3121
0.6%
3151
0.6%
3251
0.6%
3421
0.6%
3452
1.1%
3521
0.6%
3551
0.6%
3651
0.6%
ValueCountFrequency (%)
16801
0.6%
15471
0.6%
15151
0.6%
15101
0.6%
14801
0.6%
14501
0.6%
13751
0.6%
13201
0.6%
13101
0.6%
12951
0.6%

Interactions

2022-07-18T11:06:04.511889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:36.104338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:38.514512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:40.727267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.015032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.415489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.563803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:49.984464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.237562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:54.424219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.261412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.597989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.906275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:04.696283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:36.344627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:38.686847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:40.912843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.201636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.581997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.742782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:50.154912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.416958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:54.673817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.451631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.784576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.080800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:04.878803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:36.508493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:38.853057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.074458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.363864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.749814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.905211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:50.319222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.571758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:54.872808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.628146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.982117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.244857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.069004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:36.686407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.021190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.251219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.542107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.920053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.081945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:50.496453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.736202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:55.107338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.802954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:00.167787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.448552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.241912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:36.862047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.210272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.431817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.728171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.091461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.262573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:50.700258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.922487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:55.316488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.983804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:00.368336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.643754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.401522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.042847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.362606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.617969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:43.901884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.255431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.418484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:50.852170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.073035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:55.705142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:58.133925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:00.530002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.809897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.590829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.299904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.519036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.802799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:44.077969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.436095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.605978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.021017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.228884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:55.905488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:58.322728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:00.705574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:02.989701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.752727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.457374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.664625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:41.975848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:44.352442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.608571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.782080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.169744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.372676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:56.114296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:58.504513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:00.860205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:03.171533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:05.934270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.615960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.839144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:42.148553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:44.518188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.766402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:48.943323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.347481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.526083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:56.325128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:58.681865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.029052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:03.563802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:06.110152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.807530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:39.995038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:42.322256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:44.707605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:46.932743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:49.284549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.533931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.682111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:56.544522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:58.878664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.202903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:03.730050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:06.284032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:37.976506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:40.160674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:42.495936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:44.875869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.081912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:49.440549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.699677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.835211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:56.729602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.067697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.371737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:03.906476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:06.450885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:38.163221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:40.411775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:42.663820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.046043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.243132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:49.601803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:51.869047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:53.993824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:56.910450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.235739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.546369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:04.105376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:06.627170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:38.340225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:40.569294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:42.836226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:45.220548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:47.399240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:49.789787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:52.044018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:54.172311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:57.093812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:05:59.410038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:01.714951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-18T11:06:04.344278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-18T11:06:13.923457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-18T11:06:14.242416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-18T11:06:14.573608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-18T11:06:14.879874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-18T11:06:06.927736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-18T11:06:07.328821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.0
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.0
213.162.362.6718.6101.02.803.240.302.815.681.033.171185.0
314.371.952.5016.8113.03.853.490.242.187.800.863.451480.0
413.242.592.8721.0118.02.802.690.391.824.321.042.93735.0
514.201.762.4515.2112.03.273.390.341.976.751.052.851450.0
614.391.872.4514.696.02.502.520.301.985.251.023.581290.0
714.062.152.6117.6121.02.602.510.311.255.051.063.581295.0
814.831.642.1714.097.02.802.980.291.985.201.082.851045.0
913.861.352.2716.098.02.983.150.221.857.221.013.551045.0

Last rows

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
16813.582.582.6924.5105.01.550.840.391.548.6600000.741.80750.0
16913.404.602.8625.0112.01.980.960.271.118.5000000.671.92630.0
17012.203.032.3219.096.01.250.490.400.735.5000000.661.83510.0
17112.772.392.2819.586.01.390.510.480.649.8999990.571.63470.0
17214.162.512.4820.091.01.680.700.441.249.7000000.621.71660.0
17313.715.652.4520.595.01.680.610.521.067.7000000.641.74740.0
17413.403.912.4823.0102.01.800.750.431.417.3000000.701.56750.0
17513.274.282.2620.0120.01.590.690.431.3510.2000000.591.56835.0
17613.172.592.3720.0120.01.650.680.531.469.3000000.601.62840.0
17714.134.102.7424.596.02.050.760.561.359.2000000.611.60560.0